---
title: "GPUMAP: GPU-Accelerated Progressive Uniform Manifold Approximation and Projection"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.06191v1"
permalink: "https://feed.astralor.com/items/itm_622270701ee6178a33c7a853"
published: "2026-11-04T09:05:00.000Z"
score: 57
channel: "tech"
tags: ["RAG","GPUMAP","GPU-Accelerated Progressive Uniform","Manifold Approximation","Projection Dimensionality","Dimensionality","GPU-accelerated Progressive Uniform","Projection"]
---

# GPUMAP: GPU-Accelerated Progressive Uniform Manifold Approximation and Projection

## At a glance

Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings. To address this limitation, we presen…

## Key points

- Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings.
- To address this limitation, we present GPU-accelerated Progressive Uniform Manifold Approximation and Projection (GPUMAP), an efficient progressive DR algorithm designed for interactive use.
- We identify three atomic operations within UMAP and restructure them into a progressive pipeline.

## Full text

Dimensionality reduction (DR) techniques are fundamental to the analysis of high-dimensional data, yet their substantial computational cost limits their use in interactive settings. To address this limitation, we present GPU-accelerated Progressive Uniform Manifold Approximation and Projection (GPUMAP), an efficient progressive DR algorithm designed for interactive use. We identify three atomic operations within UMAP and restructure them into a progressive pipeline. As part of this adaptation, we introduce modifications to UMAP, including operation queues, instance-wise local join, and scheduling strategies. We further accelerate computation by leveraging GPU parallelism, enabling interactive analysis of million-scale datasets on commodity GPU hardware. Finally, we employ our implementation to systematically study the behavior of progressive DR, investigating how projections evolve over time relative to the ground-truth projection and how biased data streams influence the quality and stability of the resulting projections.

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